Discussion of Michael Ehrmann’s“Targeting Inflation from Below:

How Do Inflation Expectations Behave?”

Reflections on 25 Years of Inflation TargetingReserve Bank of New Zealand

December 1, 2014

Eric T. SwansonUniversity of California, Irvine

3

4

5

6

7

8

9

10

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

per

cen

t

United Kingdom

United States

Forward Nominal Interest Rates: UK vs. US

source: Gurkaynak, Levin, and Swanson (2010 JEEA)

Forward 1-year Nominal Interest Rate, from 9 to 10 Years Ahead

Great Data: 3,000 Forecast Observations

Regress Forecast Dispersion on IT Indicator

But standard errors for some coefficients look low.

t-Statistics of 10, 20, and 40 Are Too High

Comment 1: Correct for Residual Autocorrelation

Coefficient estimates shouldn’t change much, and standard errors will be correct (but larger).

Use a panel Cochrane-Orcutt procedure:

Estimate serial correlation:

Then use estimated to transform data:

But with larger standard errors, some estimates will no longer be statistically significant:

Comment 1: Correct for Residual Autocorrelation

Side Comment: Think about Month Fixed Effects

For forcast dispersion, month fixed effects are appropriate:

But for h-year-ahead forecasts, month effects make little sense:

e.g., in November 2014, forecasters should largely agree about 2014 inflation; in January 2014, not so much.

e.g., no reason to think the average forecast of inflation in 2019 should be higher or lower in November than in January.

Downward Time Trends

Forecast dispersion may have downward trend over time:• Greater central bank credibility• Greater central bank transparency

– U.S. Fed is an example (Swanson, 2006 JMCB)• Private sector learning about monetary policy• Better private sector forecasting technology• Great Moderation

Inflation Targeting observations occur later in the sample

Hard to disentangle effects of IT from these other factors

Most convincing way: some kind of control variable

GDP as a Potential Control Variable

GDP seems like a potential control variable:

• effects of IT on GDP volatility go in both directions

• net effect is probably smaller than effect of IT on inflation

But Michael’s results are 3X stronger for GDP than for inflation

Comment 2: Try to Find a Control Variable

Is there a control variable that makes the case that estimated effects are due to IT?

GDP is one such potential control variable. But it suggests that trends in other factors are more important than IT.

(Hard to argue that the effects of IT on GDP should be larger than the effects of IT on inflation.)

Michael’s empirical results would be strengthened a lot if there was a good control variable.

Central Bank Responsiveness to Inflation

Paper asserts that IT central banks need to respond less to inflation:• “if the central bank threatens to be more aggressive on inflation, it will

have to move rates by less in equilibrium.” (p. 3)• “with inflation expectations anchored at target, policy rates need to react

less to changes in inflation.” (p. 22)

𝑟 𝑡=𝑖𝑡−𝐸𝑡 𝜋 𝑡+1

If a shock causes to increase when policymakers want lower , then under IT needs to respond more, not less• e.g., some supply shocks

– “increases in oil prices today are more likely to promote consideration of increased policy ease” (Bernanke, 2003 speech)

Theoretically, this assertion is suspect:

Central Bank Responsiveness to Inflation

Empirically, the assertion is also suspect:

Comment 3: Central Bank Responsiveness

The idea that IT central banks need to respond less to inflation seems to come from “strict inflation targeting” intuition.

But this intuition may not hold for flexible inflation targeters, which is the relevant case in practice:

“For quite a few years now, however, strict inflation targeting has been without significant practical relevance. In particular, I am not aware of any real-world central bank (the language of its mandate notwithstanding) that does not treat the stabilization of employment and output as an important policy objective.” (Bernanke, 2003 speech)

The paper should soften (or at least justify better) its claims about monetary policy responsiveness to inflation.

A Few Side Comments

Definition of IT is often subjective:• UK adopted IT in 1992, but BoE not independent until 1997-8• Not clear whether or when US should be classified as IT

Adoption of IT is endogenous:• Creates sample selection problem• If high-inflation countries adopt IT, and there is mean

reversion, then effects of IT will be overestimated (Ball and Sheridan, 2005)

Today, IT is arguably just a method of communicating optimal monetary policy.Difference between IT and non-IT central banks is arguably just one of communication, not substance.

Summary

Main Comments:

1. Correct standard errors for residual autocorrelation.2. Try to find a control variable to help distinguish effects of IT

from other factors that improved over time.3. Either soften or justify claim that IT implies less central bank

responsiveness to inflation.

Minor Comments:

• Think about and clean up month fixed effects• The IT classification is subjective: check for robustness• Discuss potential problems of IT endogeneity, mean

reversion• Is the IT vs. non-IT distinction meaningful? Or is it just a

difference in transparency and communication?